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Computing Bounds on the Expected Maximum of Correlated Normal Variables

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  • Andrew M. Ross

    (Eastern Michigan University)

Abstract

We compute upper and lower bounds on the expected maximum of correlated normal variables (up to a few hundred in number) with arbitrary means, variances, and correlations. Two types of bounding processes are used: perfectly dependent normal variables, and independent normal variables, both with arbitrary mean values. The expected maximum for the perfectly dependent variables can be evaluated in closed form; for the independent variables, a single numerical integration is required. Higher moments are also available. We use mathematical programming to find parameters for the processes, so they will give bounds on the expected maximum, rather than approximations of unknown accuracy. Our original application is to the maximum number of people on-line simultaneously during the day in an infinite-server queue with a time-varying arrival rate. The upper and lower bounds are tighter than previous bounds, and in many of our examples are within 5% or 10% of each other. We also demonstrate the bounds’ performance on some PERT models, AR/MA time series, Brownian motion, and product-form correlation matrices.

Suggested Citation

  • Andrew M. Ross, 2010. "Computing Bounds on the Expected Maximum of Correlated Normal Variables," Methodology and Computing in Applied Probability, Springer, vol. 12(1), pages 111-138, March.
    • Handle: RePEc:spr:metcap:v:12:y:2010:i:1:d:10.1007_s11009-008-9097-z
    DOI: 10.1007/s11009-008-9097-z
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    References listed on IDEAS

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    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Qian, Hang, 2011. "Bayesian inference with monotone instrumental variables," MPRA Paper 32672, University Library of Munich, Germany.
    3. Vardar-Acar, Ceren & Bulut, Hatice, 2015. "Bounds on the expected value of maximum loss of fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 117-122.
    4. Qian, Hang, 2011. "Sampling Variation, Monotone Instrumental Variables and the Bootstrap Bias Correction," MPRA Paper 32634, University Library of Munich, Germany.

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